Visualization of Data Record Physicality

ABSTRACT

Systems (and corresponding methodologies) that enable inferences to be drawn from the physicality of electronic information much like that of a visual inspection of physical records are provided. In other words, a user is able to draw inferences from parameters of electronic data such as quantity, regularity, age, condition, type, keywords, title, author, origination date, storage location, etc. The innovation provides a data observation system having a summarization generator component and a rendering component that conveys attributes of electronic data such that inferences and conclusions based upon the physicality of the data.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of, and claims priority to, commonlyassigned co-pending U.S. patent application Ser. No. 12/133,213, U.S.Pat. No. 8,001,071, entitled “Visualization of Data Record Physicality,”filed on Jun. 4, 2008, the entire disclosure of which is incorporated byreference herein in its entirety.

BACKGROUND

Technological advances in computer hardware, software and networkinghave lead to increased demand for electronic information storage andexchange rather than through conventional techniques such as paper andmagnetic media, for example. Such electronic communication can providesplit-second, reliable data transfer between essentially any twolocations throughout the world. Many industries and consumers areleveraging such technology to improve efficiency and decrease costthrough web-based (e.g., on-line) services. For example, consumers canpurchase goods, review bank statements, research products and companies,obtain real-time stock quotes, download brochures, etc. with the clickof a mouse and at the convenience of home.

As the amount of available electronic data grows, it becomesincreasingly important to store and/or utilize such data in a manageablemanner that facilitates user-friendly and quick data searches andretrieval. Generally, various companies, enterprises, businesses, andthe like store electronic data that includes a tremendous amount offiles, records, image files, sound files, etc. For example, officeproductivity tools (e.g., word processing, spread sheets, presentationsoftware, mail applications, contact applications, networks, instantmessaging applications, etc.) can include a wealth of information aboutthe user as well as a user's contact lists and/or interaction withcontacts.

There are numerous advantages to electronic storage of data. Forexample, electronic data ensures backup, enhances search-ability andreduces the amount of space necessary for storage as compared tohardcopy or physical storage. Unfortunately, many of the advantages ofphysical record observations are lost when the information is convertedto electronic format. In other words, one can infer information from thevisual size, condition, markings, etc. of a hardcopy file—which isessentially lost when converted to electronic format.

SUMMARY

The following presents a simplified summary of the innovation in orderto provide a basic understanding of some aspects of the innovation. Thissummary is not an extensive overview of the innovation. It is notintended to identify key/critical elements of the innovation or todelineate the scope of the innovation. Its sole purpose is to presentsome concepts of the innovation in a simplified form as a prelude to themore detailed description that is presented later.

The innovation disclosed and claimed herein, in one aspect thereof,comprises systems (and corresponding methodologies) that enableinferences to be drawn from electronic information much like that of avisual inspection of physical records. In other words, a user is able todraw inferences from the plurality of electronic data as a factor ofquantity, regularity, age, condition, type, keywords, title, author,origination date, storage location, etc.

In an aspect of the subject innovation, a system is provided thatincludes a data observation system having a summarization generatorcomponent and a rendering component. In operation, the summarizationgenerator component inspects, evaluates and analyzes electronic data(and metadata associated therewith) to establish a summary orobservation of the data. In other words, the summarization generatorcomponent can establish observations, perceptions, estimations,patterns, etc. by way of virtually visualizing the physicality ofdocuments and drawing conclusions based upon descriptive factors andattributes.

The rendering component is capable of conveying the summary orobservation by way of a dashboard or other suitable presentation. In oneexample, a dashboard can be employed to visually render a graphicalrendition of the electronic data as it would appear if it were inphysical form. The summarization component is employed to automaticallyestablish observations and analyses based upon physical characteristicsof the data.

In aspects thereof, an artificial intelligence component and/or machinelearning & reasoning mechanism is provided that employs a probabilisticand/or statistical-based analysis to prognose or infer an action that auser desires to be automatically performed. The logic can be explicitlyor implicitly trained based upon pre-programmed rules, feedback, or thelike.

To the accomplishment of the foregoing and related ends, certainillustrative aspects of the innovation are described herein inconnection with the following description and the annexed drawings.These aspects are indicative, however, of but a few of the various waysin which the principles of the innovation can be employed and thesubject innovation is intended to include all such aspects and theirequivalents. Other advantages and novel features of the innovation willbecome apparent from the following detailed description of theinnovation when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example block diagram of a system that facilitatesestablishment of a summary of electronic data based upon the physicalityof the data.

FIG. 2 illustrates an example flow chart of procedures that facilitateestablishing an observation based upon physicality of data in accordancewith an aspect of the innovation.

FIG. 3 illustrates an example flow chart of procedures that facilitategenerating a summary of electronic data in accordance with an aspect ofthe innovation.

FIG. 4 illustrates an alternative block diagram of a system that employsan analyzer component to establish the observation of the electronicdata.

FIG. 5 illustrates an alternative block diagram of a system that employsspecialized analyzer components, an inference engine and a rules enginein accordance with aspects of the innovation.

FIG. 6 illustrates an alternative block diagram of a system that employsspecialized analyzer sub-components in accordance with an aspect of theinnovation.

FIG. 7 illustrates an example block diagram of a rendering component inaccordance with an aspect of the innovation.

FIG. 8 illustrates an example block diagram of a configuration componentin accordance with an aspect of the innovation.

FIG. 9 illustrates a block diagram of a computer operable to execute thedisclosed architecture.

FIG. 10 illustrates a schematic block diagram of an exemplary computingenvironment in accordance with the subject innovation.

DETAILED DESCRIPTION

The innovation is now described with reference to the drawings, whereinlike reference numerals are used to refer to like elements throughout.In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the subject innovation. It may be evident, however,that the innovation can be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to facilitate describing the innovation.

As used in this application, the terms “component” and “system” areintended to refer to a computer-related entity, either hardware, acombination of hardware and software, software, or software inexecution. For example, a component can be, but is not limited to being,a process running on a processor, a processor, an object, an executable,a thread of execution, a program, and/or a computer. By way ofillustration, both an application running on a server and the server canbe a component. One or more components can reside within a processand/or thread of execution, and a component can be localized on onecomputer and/or distributed between two or more computers.

As used herein, the term to “infer” or “inference” refer generally tothe process of reasoning about or inferring states of the system,environment, and/or user from a set of observations as captured viaevents and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources.

Referring initially to the drawings, FIG. 1 illustrates a block diagramof a system 100 that facilitates generation of a visual observation ofelectronic data. In examples, the system 100 can make inferences basedupon the amount of electronic data, for instance, a large amount of datacan indicate that a patient is diligent in visiting healthcareprofessionals and/or attending to medical issues. Similarly, regularityof data can further support a patient's diligence in addressing medicalissues or being proactive in preventing issues. While many of theexamples described herein are related to healthcare (e.g., health andhuman services/social services) and data associated therewith, it is tobe understood that the features, functions and benefits of theinnovation can be applied to most any industry, including, but notlimited to, legal, accounting (e.g., taxes), or the like. These andother case record examples are to be included within the scope of thisdisclosure and claims appended hereto.

Generally, system 100 includes a summarization generator component 102and a rendering component 104 that together establish observations ofelectronic data similar to those observations that can be gleaned fromphysical data. For instance, the quantity, age, condition, type, etc. ofphysical data conveys information and enables an observer to infercharacteristics and descriptions from the physical attributes (orphysicality) of the physical documents. Unfortunately, once physicaldata is converted to electronic data, many of the descriptive featuresare conventionally lost in the translation.

For example, the physicality of documents (e.g., health records) canprompt inferences about the content of the data, characteristics of thesubject (e.g., patient), regularity of the data, condition of the data,etc. In other words, in a healthcare scenario, a doctor can makeinferences based upon amount, condition, etc. of the physical records.Here, in accordance with the innovation, the summarization generatorcomponent 102 can analyze the electronic data (e.g., descriptivemetadata, content) and thereafter instruct the rendering component 104how best to render a visual depiction of the compilation of data.

In one aspect, a computer-generated image of the data can be establishedwhich incorporates or depicts descriptive characteristics. For instance,older documents can be shown on yellowed or tattered paper/files,important points in the documents can be highlighted or page cornersturned down to mark interesting documents or sections of documents.Narrative documents can appear differently from image documents, soundfiles, etc. As will be understood, the examples are too numerous tolist. Essentially, in aspects, the innovation enables or facilitates avisual rendering of electronic data such that a viewer can establishinterpretations or reach conclusions based upon the visual appearance ofthe documents. Similarly, in other aspects, the system 100 (e.g., viainferences or rules) can propose conclusions based upon interpretationsand analyses of the visual aspects of the electronic data.

FIG. 2 illustrates a methodology of interpreting visual cues associatedto electronic data in accordance with an aspect of the innovation.While, for purposes of simplicity of explanation, the one or moremethodologies shown herein, e.g., in the form of a flow chart, are shownand described as a series of acts, it is to be understood andappreciated that the subject innovation is not limited by the order ofacts, as some acts may, in accordance with the innovation, occur in adifferent order and/or concurrently with other acts from that shown anddescribed herein. For example, those skilled in the art will understandand appreciate that a methodology could alternatively be represented asa series of interrelated states or events, such as in a state diagram.Moreover, not all illustrated acts may be required to implement amethodology in accordance with the innovation.

At 202, a subject is determined. Here, a user (e.g., doctor) can specifya subject (e.g., patient) to whom data is associated. Data associated tothe subject can be located at 204. In this act, data can be associatedfrom most any location including but not limited to, local stores,remote stores, distributed sources, or the like. For example, in thehealthcare scenario, information can be gathered from various sources(e.g., heath-care entities/professionals) and thereafter consolidated oraggregated at 204.

At 206, the gathered data is analyzed, for example, metadata associatedwith the data is analyzed. Similarly, data type, origination location,origination date, etc. can be analyzed. Still further, relationshipsbetween the data elements/records, can be analyzed to determine atimeline or other relevant relationship(s). Additionally, the data canbe compared to other data, such as other patients of similardemographics, characteristics, etc. Thus, inferences can be drawn basedupon the comparisons. By way of example, an inference can be drawn basedupon the amount of data of one patient to another from a similardemographic. In other words, inferences and conclusions regardinghealthcare state, condition, diligence, etc. can be drawn from theamount of data associated to one patient versus another. As will beunderstood, many other inferences and/or conclusions can be drawn bycomparing data between subjects from similar demographics.

Referring now to FIG. 3, there is illustrated a methodology ofestablishing summary of documents (e.g., healthcare record) inaccordance with the innovation. At 302, data is received, for example,healthcare records, tax records, legal records, automobile servicerecord, etc. Additionally, the data can be received (or accessed) by wayof a crawler or other tool designed to automatically retrieve dataassociated to a subject from multiple sources, for instance, Internet-or network-based sources.

At 304, a determination is made to conclude if the document includestext data. If so, at 306, the data can be parsed and analyzed todetermine keywords and/or context of the content. Similarly, at 308, adetermination can be made to conclude if the document includes audibledata. If the document includes audible data, at 310, speech recognition(or other suitable sound analysis) mechanisms can be used to establishkeywords associated with the audible data and subsequently the contextof the keywords in view of the translated document. By way of example,if the audible data is a medical dictation, the speech recognition canbe used to translate audible speech into text, whereby, further analysiscan be performed to establish the context of the data.

Still further, at 312, a determination is made if the document containsvisible data (e.g., image data, x-rays, MRIs (magnetic resonance imagingdata)). As with text and sound described above, if visible data ispresent, key features (e.g., attributes and/or characteristics) can beestablished via pattern recognition mechanisms at 314. Patternrecognition can be employed to determine characteristics or conditionsincluded within the image such as, for example, bones, organs, etc.Thus, context of the data can be established.

Once the data is analyzed (e.g., 304-314), at 316, a summary of the datacan be generated. In other words, based upon the physicality, anobservation or summary of the data can be established at 316.Effectively, the summary enables the innovation to virtually recreatethe physical attributes of the data. Thus, inferences and conclusionscan be drawn as if the data was in its physical form rather thanconverted to electronic format.

Turning now to FIG. 4, an alternative example block diagram of system100 is shown. Generally, FIG. 4 illustrates that the summarizationgeneration component 102 can include an analyzer component 402. Theanalyzer component 402 can evaluate criteria and parameters associatedwith the data (e.g., health-related data). As described above, thisanalysis can be used to generate a visual rendering of the electronicdata and/or to draw inferences and/or conclusions based upon theresult(s) of the analysis.

As shown in FIG. 4, the analyzer component 402 can consider factors,such as, but not limited to, quantity, regularity, age, condition, type,keywords, title, author, creation date, storage location, etc.Additionally, as discussed supra, the analyzer component 402 can alsocompare the data to other data to facilitate establishment ofconclusions and inferences. These and other examples will be betterunderstood upon a review of the figures that follow infra.

FIG. 5 illustrates yet another alternative example of system 100 inaccordance with an aspect of the innovation. As depicted, the analyzercomponent 402 can include a text analyzer 502, an audio analyzer 504, animage analyzer 506, a context analyzer 508, an inference engine 510 anda rules engine 512, or combination thereof. Together, thesesub-components facilitate the ‘visual-itics’ of the innovation. The term‘visual-itics’ is intended to refer to a combination of ‘visualization’and ‘analytics.’ As described above, the innovation provides mechanismsby which inferences and conclusions can be drawn from the visualrepresentation of electronic data. In other words, much like inferencesand conclusions can be drawn from physical records (e.g., amount,condition, type . . . ), the innovation enables the same or similarinferences and conclusion to be drawn from electronic data. Theseinferences and conclusions can be drawn, whether or not the data wasoriginally captured in physical form or directly into electronic format.

The text analyzer 502, audio analyzer 504, image analyzer 506 andcontext analyzer 508 illustrated in FIG. 5 can be employed establish andevaluate the data based upon type, content, as well as other descriptivecharacteristics, attributes and/or parameters. Once these sub-components(502-508) are employed to capture information about the data, aninference engine 510 and/or a rules engine 512 can be employed to drawinferences or conclusions related to the data.

The inference engine 510 can employ artificial intelligence (AI) orother suitable machine learning & reasoning (MLR) logic whichfacilitates automating one or more features in accordance with thesubject innovation. The subject innovation (e.g., in connection withdrawing inferences from visual representations and attributes) canemploy various AI- or MLR-based schemes for carrying out various aspectsthereof. For example, a process for determining an appropriate orsuitable conclusion to be drawn from a visual representation can befacilitated via an automatic classifier system and process.

A classifier is a function that maps an input attribute vector, x=(x1,x2, x3, x4, xn), to a confidence that the input belongs to a class, thatis, f(x)=confidence(class). Such classification can employ aprobabilistic and/or statistical-based analysis (e.g., factoring intothe analysis utilities and costs) to prognose or infer an action that auser desires to be automatically performed. In the case of medicalrecords, for example, attributes can be words or phrases or otherdata-specific attributes derived from the words or phrases and theclasses can be categories or data type.

A support vector machine (SVM) is an example of a classifier that can beemployed. The SVM operates by finding a hypersurface in the space ofpossible inputs, which the hypersurface attempts to split the triggeringcriteria from the non-triggering events. Intuitively, this makes theclassification correct for testing data that is near, but not identicalto training data. Other directed and undirected model classificationapproaches include, e.g., naïve Bayes, Bayesian networks, decisiontrees, neural networks, fuzzy logic models, and probabilisticclassification models providing different patterns of independence canbe employed. Classification as used herein also is inclusive ofstatistical regression that is utilized to develop models of priority.

As will be readily appreciated from the subject specification, thesubject innovation can employ classifiers that are explicitly trained(e.g., via a generic training data) as well as implicitly trained (e.g.,via observing user behavior, receiving extrinsic information). Forexample, SVM's are configured via a learning or training phase within aclassifier constructor and feature selection module. Thus, theclassifier(s) can be used to automatically learn and perform a number offunctions, including but not limited to determining according to apredetermined criteria what conclusion(s) (or inferences) to draw basedupon a combination of data parameters and/or characteristics.

Similar to the inference engine 510, a rules engine 512 can be employedto provide the decision logic in place of, or in addition to theinference engine 510. In accordance with this alternate aspect, animplementation scheme (e.g., rule) can be applied to define and/orimplement a set of criteria by which conclusions are drawn. It will beappreciated that the rule-based implementation can automatically and/ordynamically define conclusions to be drawn from a specific set ofinformation or attributes. In response thereto, the rule-basedimplementation can make determinations by employing a predefined and/orprogrammed rule(s) based upon most any desired criteria (e.g., quantity,age, condition, type, author, storage location . . . ).

It is to be understood that rules can be preprogrammed by a user oralternatively, can be built by the system on behalf of the user.Additionally, the system (e.g., analyzer component 402) can ‘learn’ or‘be trained’ by actions of a user or group of users. In one example,once a user is provided with a visual representation of electronic data,the user can convey their interpretation of the representation to thesystem. Thus, upon subsequent renderings of the same or similar visualdata sets, the system 100 can provide an inference(s) based uponlearning from a user's past actions.

FIG. 6 illustrates an example block diagram of an analyzer component402. Specifically, FIG. 6 illustrates that the text analyzer component502 can include a language parser component 602, the audio analyzercomponent 504 can include a speech recognition (or speech to textconverter) component 604, the image analyzer component 506 can include apattern recognition component 606, and the context analyzer component508 can include 1 to N sensors 608, where N is an integer. As describedabove, each of these sub-components (602, 604, 606, 608) can be employedto establish attributes as well as a visual representation of anelectronic document or group of records. This representation need not bebased upon the content per se of the data. Rather, the representationcan be illustrative of what the documents would look like, for example,upon a desk/table, in a room, in a file cabinet, etc. As described indetail above, inferences and conclusions can be drawn from this visualdepiction of the data.

By way of example, the image analyzer component 506, together with thepattern recognition component 606 can be employed to interpret imagesincluded within a document. For example, x-rays and MRI image data canbe examined to determine scope and/or purpose of the x-ray or scan.Similarly, pattern recognition can be used to identify bones, organs,etc. within an image. These interpretations can be used by the analyzercomponent 402 to facilitate establishment of a visual rendition of thedata as well as conclusions of a patient's health, medical history, etc.

In another example, the context analyzer 508 together with the sensor(s)608 can be employed to establish contextual awareness regarding theperceived physicality of the data. For instance, if viewer is aspecialist in one field of medicine, the context analyzer 508, togetherwith the sensor(s) 608 can recognize this fact, and, accordingly, therendering of the documents and/or inferences drawn from the visualrendering can be based upon the perceived audience or specialist.

FIG. 7 illustrates an example rendering component 104 having a formatselection component 702 and a configuration component 704 includedtherein. In operation, the format selection component 702 is capable ofestablishing an appropriate format for the data. For instance, thisformat can be based upon an inference, preference, policy or the like.

In one example, the format can be an actual visual representation of thedata upon a screen or display. In this example, a virtual photograph orimage can be established that virtually places the data upon a desk ortable thereby showing the physical attributes of the electronic data. Inanother example, the physical attributes of the data can be presented ina graphical form, e.g., bar chart, pie chart or the like. In either ofthese examples, the physicality of electronic data can be formatted forrendering upon a dashboard (or other display).

The configuration component 704 can be employed to manage the layoutand/or organization of the day upon a display. In other words, theconfiguration component 704 can be employed to comprehensively arrangeor otherwise organize the data for presentation to a user. In examples,the configuration component 704 can be employed to establish therendering of the electronic data in accordance with the format selectedor otherwise in accordance with the display apparatus or device.

FIG. 8 illustrates an example configuration component 704 that includesa filter component 802 and a layout component 804. Each of thesesub-components (802, 804) is employed to render a comprehensivepresentation of the physicality of electronic documents. Each of thesecomponents can be employed to affect the rendering of the physicality ofelectronic documents in accordance with the innovation. For instance, inaspects, the sub-components (802, 804) can be employed to establish apersonalized observation of the physicality of electronic documents. Asdescribed above, the innovation can learn from a viewer's behavior orotherwise render in accordance with preprogrammed logic or rules.

The filter component 802 can be employed to filter information asdesired or appropriate. For example, if a specialist if interesting inmaking interpretations from the physicality of a patient's record, therecord can be filtered based upon the viewer's specialty. In otherwords, if the identity of a viewer reveals that they are a radiologist,the record can be filtered based upon images (e.g., x-rays, scans) andother pertinent documents included within the record. Similarly, ifdesired, upon viewing the virtual physical rendering of set ofdocuments, the filter component 802 can enable a user to ‘drill-down’into a particular document or set of documents so as to glean or extractinferences and conclusions regarding the particular document or group ofdocuments.

The layout component 804 can be employed to position or otherwiseorganize the virtual physical rendering of the electronic documents. Inother words, documents can be grouped based upon most any criteria,including but not limited to, age, condition, type, size, etc.Accordingly, these documents can be displayed upon a monitor (or displaydevice) or virtually positioned upon a desk or table as appropriate ordesired.

Referring now to FIG. 9, there is illustrated a block diagram of acomputer operable to execute the disclosed architecture. In order toprovide additional context for various aspects of the subjectinnovation, FIG. 9 and the following discussion are intended to providea brief, general description of a suitable computing environment 900 inwhich the various aspects of the innovation can be implemented. Whilethe innovation has been described above in the general context ofcomputer-executable instructions that may run on one or more computers,those skilled in the art will recognize that the innovation also can beimplemented in combination with other program modules and/or as acombination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the innovation may also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

A computer typically includes a variety of computer-readable media.Computer-readable media can be any available media that can be accessedby the computer and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer-readable media can comprise computer storage mediaand communication media. Computer storage media includes both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by the computer.

Communication media typically embodies computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism, and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of the anyof the above should also be included within the scope ofcomputer-readable media.

With reference again to FIG. 9, the exemplary environment 900 forimplementing various aspects of the innovation includes a computer 902,the computer 902 including a processing unit 904, a system memory 906and a system bus 908. The system bus 908 couples system componentsincluding, but not limited to, the system memory 906 to the processingunit 904. The processing unit 904 can be any of various commerciallyavailable processors. Dual microprocessors and other multi-processorarchitectures may also be employed as the processing unit 904.

The system bus 908 can be any of several types of bus structure that mayfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 906 includesread-only memory (ROM) 910 and random access memory (RAM) 912. A basicinput/output system (BIOS) is stored in a non-volatile memory 910 suchas ROM, EPROM, EEPROM, which BIOS contains the basic routines that helpto transfer information between elements within the computer 902, suchas during start-up. The RAM 912 can also include a high-speed RAM suchas static RAM for caching data.

The computer 902 further includes an internal hard disk drive (HDD) 914(e.g., EIDE, SATA), which internal hard disk drive 914 may also beconfigured for external use in a suitable chassis (not shown), amagnetic floppy disk drive (FDD) 916, (e.g., to read from or write to aremovable diskette 918) and an optical disk drive 920, (e.g., reading aCD-ROM disk 922 or, to read from or write to other high capacity opticalmedia such as the DVD). The hard disk drive 914, magnetic disk drive 916and optical disk drive 920 can be connected to the system bus 908 by ahard disk drive interface 924, a magnetic disk drive interface 926 andan optical drive interface 928, respectively. The interface 924 forexternal drive implementations includes at least one or both ofUniversal Serial Bus (USB) and IEEE 1394 interface technologies. Otherexternal drive connection technologies are within contemplation of thesubject innovation.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 902, the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer, such as zipdrives, magnetic cassettes, flash memory cards, cartridges, and thelike, may also be used in the exemplary operating environment, andfurther, that any such media may contain computer-executableinstructions for performing the methods of the innovation.

A number of program modules can be stored in the drives and RAM 912,including an operating system 930, one or more application programs 932,other program modules 934 and program data 936. All or portions of theoperating system, applications, modules, and/or data can also be cachedin the RAM 912. It is appreciated that the innovation can be implementedwith various commercially available operating systems or combinations ofoperating systems.

A user can enter commands and information into the computer 902 throughone or more wired/wireless input devices, e.g., a keyboard 938 and apointing device, such as a mouse 940. Other input devices (not shown)may include a microphone, an IR remote control, a joystick, a game pad,a stylus pen, touch screen, or the like. These and other input devicesare often connected to the processing unit 904 through an input deviceinterface 942 that is coupled to the system bus 908, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, etc.

A monitor 944 or other type of display device is also connected to thesystem bus 908 via an interface, such as a video adapter 946. Inaddition to the monitor 944, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 902 may operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 948. The remotecomputer(s) 948 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer902, although, for purposes of brevity, only a memory/storage device 950is illustrated. The logical connections depicted include wired/wirelessconnectivity to a local area network (LAN) 952 and/or larger networks,e.g., a wide area network (WAN) 954. Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which mayconnect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 902 is connectedto the local network 952 through a wired and/or wireless communicationnetwork interface or adapter 956. The adapter 956 may facilitate wiredor wireless communication to the LAN 952, which may also include awireless access point disposed thereon for communicating with thewireless adapter 956.

When used in a WAN networking environment, the computer 902 can includea modem 958, or is connected to a communications server on the WAN 954,or has other means for establishing communications over the WAN 954,such as by way of the Internet. The modem 958, which can be internal orexternal and a wired or wireless device, is connected to the system bus908 via the serial port interface 942. In a networked environment,program modules depicted relative to the computer 902, or portionsthereof, can be stored in the remote memory/storage device 950. It willbe appreciated that the network connections shown are exemplary andother means of establishing a communications link between the computerscan be used.

The computer 902 is operable to communicate with any wireless devices orentities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,restroom), and telephone. This includes at least Wi-Fi and Bluetooth™wireless technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from acouch at home, a bed in a hotel room, or a conference room at work,without wires. Wi-Fi is a wireless technology similar to that used in acell phone that enables such devices, e.g., computers, to send andreceive data indoors and out; anywhere within the range of a basestation. Wi-Fi networks use radio technologies called IEEE 802.11(a, b,g, etc.) to provide secure, reliable, fast wireless connectivity. AWi-Fi network can be used to connect computers to each other, to theInternet, and to wired networks (which use IEEE 802.3 or Ethernet).Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radio bands, atan 11 Mbps (802.11a) or 54 Mbps (802.11b) data rate, for example, orwith products that contain both bands (dual band), so the networks canprovide real-world performance similar to the basic 10BaseT wiredEthernet networks used in many offices.

Referring now to FIG. 10, there is illustrated a schematic block diagramof an exemplary computing environment 1000 in accordance with thesubject innovation. The system 1000 includes one or more client(s) 1002.The client(s) 1002 can be hardware and/or software (e.g., threads,processes, computing devices). The client(s) 1002 can house cookie(s)and/or associated contextual information by employing the innovation,for example.

The system 1000 also includes one or more server(s) 1004. The server(s)1004 can also be hardware and/or software (e.g., threads, processes,computing devices). The servers 1004 can house threads to performtransformations by employing the innovation, for example. One possiblecommunication between a client 1002 and a server 1004 can be in the formof a data packet adapted to be transmitted between two or more computerprocesses. The data packet may include a cookie and/or associatedcontextual information, for example. The system 1000 includes acommunication framework 1006 (e.g., a global communication network suchas the Internet) that can be employed to facilitate communicationsbetween the client(s) 1002 and the server(s) 1004.

Communications can be facilitated via a wired (including optical fiber)and/or wireless technology. The client(s) 1002 are operatively connectedto one or more client data store(s) 1008 that can be employed to storeinformation local to the client(s) 1002 (e.g., cookie(s) and/orassociated contextual information). Similarly, the server(s) 1004 areoperatively connected to one or more server data store(s) 1010 that canbe employed to store information local to the servers 1004.

What has been described above includes examples of the innovation. Itis, of course, not possible to describe every conceivable combination ofcomponents or methodologies for purposes of describing the subjectinnovation, but one of ordinary skill in the art may recognize that manyfurther combinations and permutations of the innovation are possible.Accordingly, the innovation is intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

1. A system comprising: a summarization component configured to generatean image of a document as electronic data; a rendering componentconfigured to present a visual rendition of the electronic data, thevisual rendition indicating a physical representation illustratingimportance of content in the document; a context analyzer configured torecognize an identity of a viewer of the visual rendition, the identityof the viewer being a specialist; and an inference engine configured toprovide mechanisms by which inferences and interpretations are drawnbased upon the importance of the content in the visual rendition and anindication the viewer is a specialist.
 2. The system of claim 1, whereinthe physical representation illustrating the importance of the contentin the document is by highlighting points in the document.
 3. The systemof claim 1, wherein the physical representation illustrating theimportance of the content in the document is by turning down a corner ofthe document to identify important sections in the document.
 4. Thesystem of claim 1, wherein the inference engine further uses a mechanismof a support vector machine to classify the electronic data based on theimportance of the content.
 5. The system of claim 1, wherein theinference engine uses the mechanisms including at least one of a patternmatching, a naïve Bayes classifier, a decision tree, a neural network, afuzzy logic model, or a probabilistic classification model
 6. The systemof claim 1, further comprising a rules engine configured to define orimplement a set of criteria by which the inferences or theinterpretations are drawn.
 7. The system of claim 1, further comprisinga text analyzer component configured to parse text in the electronicdata, wherein the parsed text identifies the importance of the contentbased on the highlighted points in the document or the corner of thedocument being turned down.
 8. The system of claim 1, further comprisingan image analyzer component configured to recognize patterns included inthe electronic data, wherein the recognized patterns contribute to theimportance of the content of the electronic data.
 9. The system of claim1, further comprising a format selection component configured todetermine an appropriate format for the visual rendition based at leastin part on an inference, a preference, a rule, a preprogrammed logic, ora policy.
 10. The system of claim 1, further comprising a configurationcomponent configured to arrange or organize the electronic data forpresentation to the specialist.
 11. A method implemented at leastpartially by a processing unit, the method comprising: gatheringelectronic data of documents associated with patients, the documentsaggregated from multiple sources; providing a visual representation ofthe electronic data; analyzing the electronic data based on type,origination location, or origination data; comparing the electronic dataassociated with the patients of similar demographics based on the visualrepresentation; and drawing inferences based on the comparison of thevisual representation.
 12. The method of claim 11, wherein the analyzingthe electronic data includes determining timelines for the electronicdata.
 13. The method of claim 11, wherein the analyzing the electronicdata includes identifying relationships from the electronic data. 14.The method of claim 11, wherein the comparing the electronic data basedon the visual representation includes an amount of data associated witha patient.
 15. The method of claim 11, wherein the inferences include atleast one of a healthcare state or a condition.
 16. One or morecomputer-readable storage media encoded with instructions that, whenexecuted by a processor, perform acts comprising: gathering electronicdata of documents associated with patients; providing a visualrepresentation of the electronic data to illustrate an importance ofcontent in the documents; identifying a viewer of the visualrepresentation is a provider to patients; and analyzing the electronicdata between the patients from similar demographics to draw inferencesor conclusions based on the content in the visual representation and anindication that the viewer is the provider.
 17. The computer-readablestorage media of claim 16, wherein the importance of content in thedocuments includes highlighting points in the documents.
 18. Thecomputer-readable storage media of claim 16, wherein the importance ofthe content in the documents includes turning down a corner of thedocuments to identify important sections in the documents.
 19. Thecomputer-readable storage media of claim 16, further comprisingestablishing a set of conclusions based on the visual representation ofthe electronic data, wherein the set of conclusions are based upon a setof pre-programmed rules.
 20. The computer-readable storage media ofclaim 16, further comprising inferring a set of conclusions based onidentifying the importance of content in the electronic data, whereinthe set of conclusions are based on statistical or historical analysis.